Volume 1,Issue 4
Fall 2025
数据整合驱动的极大似然估计教学改革探索——以转录动力学建模为例
极大似然估计（MLE）是统计推断中的核心方法，广泛应用于生命科学数据建模。随着现代生物技术的发展，实验数据呈现多样化特征，如何有效整合不同类型的数据以提高MLE准确性，已成为统计建模与生命科学交叉研究中的重要问题。本文以转录动力学为例，探讨如何通过整合nascent RNA表达数据与转录启动时间数据，精确估计随机动力学模型中的关键参数，以增强学生对数据驱动建模的理解，为相关课程的教学改革提供思路。
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